Professor Norman Beaulieu
Beijing University of Postal & Telecommunications

A New Cramér-Rao Bound for White Gaussian Noise 

Time & Date: 9:00am-10:00am, Thursday June 8, 2017
Location: EME 1151, UBC Okanagan campus
Registration is open now: https://events.vtools.ieee.org/m/45794 

Talk Abstract: 

The Cramér-Rao (CR) lower bound is ubiquitous in signal processing and communications, finding application in wireless communications, optical communications, RF engineering, data communications, speech and image processing and biomedical engineering, for example. It is the universal measure of the goodness of an estimator or a predictor in these fields, and it is considered fundamental in mathematical statistics. Of a plethora of application areas, we mention only channel state prediction, signal-to-noise ratio, mean-square-error, symbol and frame estimation, as well as carrier phase, carrier frequency, and carrier amplitude estimations. Yet, the Cramér-Rao lower bound has severe limitations and shortcomings. Despite these disadvantages, the CR bound continues to find widespread application as the quality measure of choice in prediction, estimation and detection research and in engineering practice. This is probably because known alternatives to the CR bound are considered highly complex and are thought to be much less simple to apply to practical problems in prediction, estimation and detection. The CR bound is universally expressed in discrete time with the number of “independent samples” as a parameter. Meanwhile, the
number of independent samples is dictated (and without compromise) by the bandwidth and the frequency characteristic of the filtering in the circuitry, and the performance of the CR bound is dependent on the signal characteristics.

In this talk, a fundamental expression of the CRB for a known signal in additive white Gaussian noise is determined. The analysis starts with a discussion on how many independent samples can be obtained from the observed signal, leading to an integral format of the CRB. The continuous-time CRB is proved to be the optimal CRB. The optimum CRB for white Gaussian noise is fundamentally expressed as the noise power spectral density over the energy of the
derivative, with respect to the parameter of estimation, of the observed signal. The CRBs for the amplitude, the carrier frequency, the Doppler shift frequency and the phase shift of a carrier signal are presented. A detailed example of carrier phase estimation using the new form of the CRB is given.​

Speaker Biography: 

Dr. Norman C. Beaulieu received the B.A.Sc. (honours), MASc., and Ph.D. degrees in electrical engineering from the University of British Columbia, Canada in 1980, 1983, and 1986, respectively. He was awarded the University of British Columbia Special University Prize in Applied Science in 1980 as the highest standing graduate in the Faculty of Applied Science.

Dr. Beaulieu was a Queen’s National Scholar Assistant Professor with the Department of Electrical Engineering, Queen’s University, Canada from 1986 to 1988, an Associate Professor from 1988 to 1993, and a Professor from July 1993 to 2000. In 2000, he became the iCORE Research Chair in Broadband Wireless Communications at the University of Alberta, Edmonton, Alberta, Canada, in 2001, the Canada Research Chair in Broadband Wireless Communications, and in 2010 the AITF Research Chair in Broadband Wireless Communications.

Dr. Beaulieu received the Natural Science and Engineering Research Council of Canada (NSERC) E.W.R. Steacie Memorial Fellowship in 1999. He was elected a Fellow of the Engineering Institute of Canada in 2001, a Fellow of the Royal Society of Canada in 2002 and a Fellow of the Canadian Academy of Engineering in 2006. In 2004, he was awarded the Medaille K.Y. Lo Medal of the Engineering Institute of Canada. He was awarded the Thomas W. Eadie Medal of the Royal Society of Canada in 2005, as well as the Alberta Science and Technology Leadership Foundation ASTech Outstanding Leadership in Alberta Technology Award. He was the 2006 recipient of the J. Gordin Kaplan Award for Excellence in Research, the University of Alberta’s most prestigious research prize. Dr. Beaulieu is listed on ISIHighlyCited.com and was an IEEE Communications Society Distinguished Lecturer in 2007/2008. He is the recipient of the IEEE Communications Society 2007 Edwin Howard Armstrong Achievement Award. Dr. Beaulieu is the recipient of both the 2010 R.A. Fessenden Silver Medal and the 2010 Canadian Award in Telecommunications. In 2011, he was awarded the IEEE Communications Society Radio Communications Committee Technical recognition Award, and in 2013, the Signal Processing and Communications Electronics Technical Committee (Inaugural) Technical Recognition Award. In 2014, he was awarded the IEEE CTTC (Communication Theory Technical Committee) Personal recognition Award, and in 2015, he was recruited as the “Thousand Plan Professor” by Beijing University of Postal & Telecommunications in China.

For further information please contact: Julian Cheng (email: Julian.Cheng at ubc.ca)
Refreshments will be provided

Professor Norman Beaulieu
Beijing University of Postal & Telecommunications

The Absolute Error Power Detectors

Time & Date: 3:30pm-4:30pm, Thursday June 1, 2017
Location: EME 1101, UBC Okanagan campus
Registration is open now: https://events.vtools.ieee.org/m/45726

Talk Abstract:
It is well known and fundamental that the matched filter is the optimal detector for a signal immersed in additive white Gaussian noise. The matched filter is a continuous-time structure and always performs better than digital matched filters, which are optimal structures for detecting signals in additive white Gaussian noise based on a number of independent samples of the signal-plus-noise. In the case of non-Gaussian noise, only one other optimal detection structure is known, and that is the optimal (continuous-time) detector for signals immersed in Laplace noise. Meanwhile, the fundamental Gaussian distribution is a special case of the more flexible and descriptive generalized Gaussian distribution (GGD). In this talk, we derive the optimal detector for a signal immersed in additive GGD noise, which we dub the generalized matched filter. This detection scheme finds the absolute value of the difference between a replica of the transmitted signal and the received signal-plus noise, raises this absolute error to the βth power, and then integrates the resulting signal. This detection structure can, therefore, also be referred to as the absolute error power detector. We show that the matched filter is a special case of the absolute error power detector for GGD parameter β = 2, the Gaussian noise case, and that the optimal detector for Laplace noise is also a special case when β = 1. The optimal probability of error for binary signaling in additive white generalized Gaussian noise is assessed. The fundamental structure is also optimal for higher-level modulations after straightforward extensions.

Speaker Biography:
Dr. Norman C. Beaulieu received the B.A.Sc. (honours), MASc., and Ph.D. degrees in electrical engineering from the University of British Columbia, Canada in 1980, 1983, and 1986, respectively. He was awarded the University of British Columbia Special University Prize in Applied Science in 1980 as the highest standing graduate in the Faculty of Applied Science.

For further information please contact: Julian Cheng (email: Julian.Cheng at ubc.ca)
Refreshments will be provided

Dr. Kenichi Sugihara
Gifu Keizai University
Gifu Pref. Japan

Automatic generation of 3D building models

Time & Date: 5 pm—6:00 pm, Monday, March 20th, 2017
Location: E 102, 1000 KLO Rd., Okanagan College, Kelowna, BC
Registration is open now: https://events.vtools.ieee.org/m/44551

Talk Abstract: A 3D urban model is an important information infrastructure that can be utilized in several fields, such as, urban planning and game industries. However, enormous time and effort have to be spent to create 3D urban models, using 3D modeling software such as 3ds Max or SketchUp. In our research we will employ automatic generation of 3D building models through integrating GIS (Geographic Information System) and CG (Computer Graphics). An integrated system is proposed for automatically creating 3D building models from building polygons (building footprints) on a digital map. Since most building polygons’ edges meet at a right angle (orthogonal polygon), a complicated orthogonal building polygon can be partitioned into a set of rectangles. The integrated system partitions orthogonal building polygons into a set of rectangles and places rectangular roofs and box-shaped building bodies on these rectangles. This proposal seeks to implement a novel approach to 3D building model construction for rapid assessment of roof damage and insurance liability in the aftermath of natural disasters.

The research will achieve two objectives:

1. The integration of GIS and 3DCG (3D Computer Graphics) components in a new extension for the ArcGIS platform that generates both simple and complex 3D house models from building footprints (building polygons).
2. Automated generation of simple and complex roof geometries for rapid roof area damage reporting by length measurements and area calculations of all roof surfaces. In addition to the application to the assessment of roof damage, this system can be applied to BIM (Building information modelling: 3d building model whose parts are linked to each attribute, and by which collision detection between parts can be made).

Speaker Biography: Dr. Kenichi Sugihara, Professor (Doctor of Engineering) of Gifu Keizai University in Gifu Pref. Japan. Kenichi graduated from the graduate school of Nagoya University in 1979 and in 2001. He was working at Panasonic for 7 years and at Sony for 3 years as an built-in micro-computer engineer. He specialized in computer science in CG and GIS and in automatic generation of 3-D urban models by the integration of GIS and CG based on digital maps. He has started studying of civil engineering, specifically urban planning and disaster prevention, where computer science is of great use, especially CG and GIS. The research in engineering and urban planning provides him government subsidies additionally.

For further information please contact: Youry Khmelevsky (email: youry@ieee.org)
Refreshments will be provided

Dr. R. Zoughi
Applied Microwave Nondestructive Testing Laboratory (amntl)
Electrical and Computer Engineering Department
Missouri University

Evolution of Microwave and Millimeter Wave Imaging for NDE Applications

Co-sponsored by IEEE IMS TC-36

Time & Date: 9:40 am—11:00 am, Friday, March 17th, 2017
Location: ADM 026, UBC, Okanagan Campus, Kelowna, BC
Registration is open now: https://events.vtools.ieee.org/m/44113
and
Time & Date: 4:00 pm—5:00 pm, Friday, March 17th, 2017 (CANCELLED)
Location: E 103, Okanagan College, 1000 KLO Rd., Kelowna, BC
Registration is open now: https://events.vtools.ieee.org/m/44505

Talk Abstract: Millimeter-wave signals span the frequency range of 30 GHz to 300 GHz, corresponding to a wavelength range of 10 mm to 1 mm. Signals at these frequencies can easily penetrate inside dielectric materials and composites and interact with their inner structures. The relatively small wavelengths and wide bandwidths associated with these signals enable the production of high spatial-resolution images of materials and structures. Incorporating imaging techniques such as lens-focused and near-field techniques, synthetic aperture focusing, holographical methods based on robust back-propagation algorithms with more advanced and unique millimeter wave imaging systems have brought upon a flurry of activities in this area and in particular for nondestructive evaluation (NDE) applications. These imaging systems and techniques have been successfully applied for a wide range of critical NDE-related applications.

Although, near-field techniques have also been prominently used for these applications in the past, undesired issues related to changing standoff distance have resulted in several innovative and automatic standoff distance variation removal techniques. Ultimately, imaging techniques must produce high-resolution 3D images, become real-time, and be implemented using portable systems. To this end and to expedite the imaging process while providing a high-resolution images, the design and demonstration of a 6″ by 6″ one-shot, rapid and portable imaging system (Microwave Camera), consisting of 576 resonant slot elements, was completed in 2011. Subsequently, efforts were expended to design and implement several different variations of this imaging system to accommodate one-sided and mono-static imaging, while enabling 3D image production using non-uniform rapid scanning of an object, as well as increasing the operating frequency into higher millimeter wave frequencies. These efforts have led to the development of a real-time, portable, high-resolution and 3D imaging microwave camera operating in the 20-30 GHz frequency range which was recently completed. This presentation provides an overview of these techniques, along with illustration of several typical examples where these imaging techniques have effectively provided viable solutions to many critical NDE problems.

Speaker Biography: R. Zoughi received his B.S.E.E, M.S.E.E, and Ph.D. degrees in electrical engineering (radar remote sensing, radar systems, and microwaves) from the University of Kansas where from 1981 until 1987 he was at the Radar Systems and Remote Sensing Laboratory (RSL). Subsequently, in 1987 he joined the Department of Electrical and Computer Engineering at Colorado State University (CSU), where he established the Applied Microwave Nondestructive Testing Laboratory (amntl). He held the position of Business Challenge Endowed Professor of Electrical and Computer Engineering from 1995 to 1997 while at CSU. In 2001 he joined the Department of Electrical and Computer Engineering at Missouri University of Science and Technology (S&T), formerly University of Missouri-Rolla (UMR), as the Schlumberger Distinguished Professor. His current areas of research include developing new nondestructive techniques for microwave and millimeter wave testing and evaluation of materials (NDT&E), developing new electromagnetic probes and sensors to measure characteristic properties of material at microwave frequencies, developing embedded modulated scattering techniques for NDT&E purposes and real-time high resolution imaging system development. He is the author of a book entitled “Microwave Nondestructive Testing and Evaluation Principles”, and the co-author of a chapter on Microwave Techniques in an undergraduate introductory textbook entitled “Nondestructive Evaluation: Theory, Techniques, and Applications”. He has been the recipient of numerous teaching awards both at CSU and Missouri S&T. He is the co-author of over 585 journal papers, conference proceedings and presentations and technical reports. He has eighteen patents to his credit all in the field of microwave nondestructive testing and evaluation. He was the recipient of the 2007 IEEE Instrumentation and Measurement Society Distinguished Service Award, the 2009 American Society for Nondestructive Testing (ASNT) Research Award for Sustained Excellence, and the 2011 IEEE Joseph F. Keithley Award in Instrumentation & Measurement. In 2013 he and his co-authors received the H. A.

Wheeler Prize Paper Award of the IEEE Antennas and Propagation Society (APS) related to the design of 24 GHz real-time microwave camera. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the American Society for Nondestructive Testing (ASNT), and served as the Editor-in-Chief of the IEEE Transactions on Instrumentation and Measurement (2007-2011) and the President of the IEEE Instrumentation and Measurement Society (2014-2015).

For further information please contact: Dr. Zheng Liu <zheng.liu@ubc.ca> and
Youry Khmelevsky (email: youry@ieee.org)
Refreshments will be provided

Md Shahrear Iqbal
School of Computing,
Queen’s University, Kingston, Canada

Secure Context Detection in Smartphones

Time & Date: 5:30 pm–6:30 pm, Thursday, February 16th, 2017
Location: EME 1151, UBC, Okanagan Campus, Kelowna, BC

Talk Abstract: The availability of powerful smartphones and the necessity of security in mobile devices have made researchers propose multiple security modes (e.g., home, office, outdoor, and financial) for such devices. In each mode, a user can install a different set of apps. However, in most of the cases, the user has to select the mode manually. If we can sense the smartphone’s security context accurately, then it is possible to switch between different security modes automatically. Also, smartphone operating systems are becoming ubiquitous. As a result, mobile apps need to behave differently based on the security context (e.g., not sending the data if the network is insecure). In this talk, we focus on sensing different security parameters (e.g., location, is-network-encrypted) and calculating the security context from the parameters. We also talk about a security context management framework that maintains a cache of security contexts and parameters to be used by the operating system and third-party applications. As detecting contexts requires the use of power-hungry smartphone sensors, a comprehensive framework for sharing security parameters among various applications can be beneficial in terms of energy and other resource expenses.

Speaker Biography: Md Shahrear Iqbal is a Ph.D. candidate in the School of Computing, Queen’s University,Canada, where he is a member of the Queen’s Reliable Software Technology (QRST) research group. He also holds a faculty position in the Department of Computer Science and Engineering (CSE) in Bangladesh University of Engineering and Technology (BUET), Bangladesh. He is currently doing research on the security of smart devices. He is also interested in future internet and cyber-security of smart cities. He obtained his Bachelor’s and Master’s degree in CSE from BUET. More information about his research and publications can be found at http://www.cs.queensu.ca/~iqbal.

For further information please contact: Dr. Md. Jahangir Hossain (jahangir.hossain@ubc.ca) and
Youry Khmelevsky (email: youry@ieee.org)
Registration is open now: https://events.vtools.ieee.org/m/43547
Refreshments will be provided

Paige Tyler and Tim Sayler
Computer Science, Okanagan College

Machine Learning Workshop

Time & Date: 1:30 pm–4 pm, Thursday, February 2nd, 2017
and
Time & Date: 4:30 pm–7 pm, Thursday, February 2nd, 2017 (evening session)
Location: E107, Okanagan College, 1000 KLO Rd., Kelowna, BC V1Y 4X8

Talk Abstract: Machine learning is a category of computer science that involves the prediction of data based on the observation of patterns in previously gathered data. Microsoft has recently released Azure, a cloud computing service that makes machine learning algorithms available building, deploying and managing applications through Microsoft data centers. This workshop will start off with an overview of machine learning and its algorithms. We will then guide you on the basics of building and deploying some simple experiments and then break you off into groups to attempt your own experiments.

A representative from Microsoft Vancouver will participate in workshop.

What to Bring: a laptop

Speaker Biographies:
Paige Tyler is a second year student in the Computer Information Systems degree at the Okanagan College with an interest in machine learning. She has spent some time exploring Microsoft’s Azure Machine Learning service as a research assistant.

Tim Sayler is a third-year student studying in the degree program of Computer Information Systems at Okanagan College. His primary focus is software engineering, as well as machine learning and collaborating in an agile team environment. He has over 10 years of previous industry experience and works as a research assistant while attending Okanagan College.

Registration is open now: https://events.vtools.ieee.org/m/42254
Pizza will be provided after the talk. For further information please contact:
Youry Khmelevsky (email: youryNOSPAM@ieee.org)

McCall Milligan, Jim Nastos and
Jan O’Brien
Department of Institutional Research, Okanagan College

Data Analytics in Educational Institutions:
Building a Predictive Model for Student Enrolment

 Time & Date: 4pm–5pm, Wednesday, December 7th, 2016
Location: E103, Okanagan College, 1000 KLO Rd., Kelowna, BC V1Y 4X8

Talk Abstract: Now more than ever, planning and decision-making is data, rather than gut, driven. While there are many existing models for regression, prediction and learning, choosing an appropriate model involves understanding the associated data deeply. Often, choosing a standard model is still insufficient if the analyst is not customizing their model to handle the intricacies of their data.

We describe our development process of a Markov chain and Dynamic Markov Chain-based model for predicting college enrolments. The model is compared and contrasted against previously-attempted models. We include a demo of using our interface into the model, which allows for roughly 600 configurations of parameters, and many additional override value options.

The ideas presented here are applicable to many dynamical systems involving population migration in a discrete-time process.

Speaker Biography: Jim Nastos earned his PhD from UBC Okanagan in Interdisciplinary Studies. He is currently a College Professor of Computer Science at Okanagan College, and previously worked as an Institutional Research Data Analyst at Okanagan College, a math and computer science lecturer at the UBCO and a math lecturer at the UAlberta. His academic research has been published in mathematics, computer science, physics, social networks, bioinformatics and marketing venues.

McCall Milligan earned his bachelor of Applied Mathematics at UBC Okanagan. He will be entering an MSc program in financial modeling and risk analysis in 2017. McCall is currently working as an Analyst at Okanagan College in the Institutional Research and Planning office.

Jan O’Brien is Manager of Institutional Research and Planning at Okanagan College. She has been in higher education for over 20 years, in Faculty, co-op education and administration. The department works with data to support planning and evaluation of college programming. Jan earned a Master of Science degree from the University of Leicester and a bachelor of business administration degree from Simon Fraser University. Before working in higher education, Jan worked in technology marketing in Vancouver.

Registration is open now: https://events.vtools.ieee.org/m/42254
Pizza will be provided after the talk. For further information please contact:
Youry Khmelevsky (email: youryNOSPAM@ieee.org)